Browsing by Author "Talbi, Billel"

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    An experimental assessment of direct torque control and model predictive control methods for induction machine drive
    (IEEE, 2018) Ammar, Abdelkarim; Kheldoun, Aissa; Metidji, Brahim; Talbi, Billel; Ameid, Tarek; Azzoug, Younes
    Finite-State Model Predictive Control methods (FS-MPC) have been presented recently in the field of electrical drive and power electronics as an alternative to the conventional strategies. This paper presents a comparative evaluation between Direct Torque Control (DTC) and two finite-state model predictive control strategies applied to induction motor drive. Both DTC and MPC are nonlinear control techniques which dispense with the use of modulation unit (i.e. pulse width modulator (PWM) or space vector modulator (SVM)). DTC can provide good decoupled flux and torque control using pair of hysteresis comparators and look-up switching table for voltage vectors selection. In contrast with the model predictive control which includes the inverter model in control design. The optimal selection of inverter switching states minimizes the error between references and the predicted values of control variables by the optimization of a cost function. The effectiveness of applied algorithms is investigated by an experimental implementation using real-time interface (RTI) based on dSpace 1104
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    Predictive direct torque control with reduced ripples for induction motor drive based on T‐S fuzzy speed controller
    (2019) Ammar, Abdelkarim; Talbi, Billel; Ameid, Tarek; Azzoug, Younes; Kerrache, Abdelaziz
    Finite-state model predictive control (FS-MPC) has been widely used for controlling power converters and electric drives. Predictive torque control strategy (PTC) evaluates flux and torque in a cost function to generate an optimal inverter switching state in a sampling period. However, the existing PTC method relies on a traditional proportional-integral (PI) controller in the external loop for speed regulation. Consequently, the torque reference may not be generated properly, especially when a sudden variation of load or inertia takes place. This paper proposes an enhanced predictive torque control scheme. A Takagi-Sugeno fuzzy logic controller replaces PI in the external loop for speed regulation. Besides, the proposed controller generates a proper torque reference since it plays an important role in cost function design. This improvement ensures accurate tracking and robust control against different uncertainties. The effectiveness of the presented algorithms is investigated by simulation and experimental validation using MATLAB/Simulink with dSpace 1104 real-time interface